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Measuring Loneliness Among Middle-Aged and Older Adults: The UCLA and de Jong Gierveld Loneliness Scales

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Abstract

This study examined the measurement and invariance properties of the R-UCLA and de Jong Gierveld loneliness scales for research involving middle-aged and older adults. Confirmatory factor analyses (CFAs) of data from interviews with adults aged 45–84 revealed limited support for the unidimensionality of either scale while subsequent analyses indicated the multidimensionality of both. However, method effects associated with positive and negative item wording were also evident. Multiple group CFAs provided limited support for assumptions of measurement invariance across age groups and from baseline to follow-up with regard to the R-UCLA scale. In contrast, strong measurement invariance across age groups and successive measurements was established for the bi-dimensional dJG scale. Overall, the findings supported the relative utility of the dJG scale for research involving middle-aged and older adults but suggested a need for attention to the implications of method effects associated with item wording and lack of measurement invariance with respect to item residuals.

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Notes

  1. The response rate for the telephone screening survey was 39.2 % using Statistics Canada and MRIA guidelines (see http://www.mria-arim.ca/STANDARDS/Response.asp). Although somewhat low, the denominator includes those whose eligibility to participate in the study was unknown (i.e., those who we were unable to reached by telephone, who were hospitalized or unable to participate for health or language reasons, who refused to participate in the screening interview but who may have been ineligible to participate in the study due to their age, etc.).

  2. Testing configural invariance involved a baseline model (M0) with identical items to factors assignment but freely estimated factor loadings, intercepts, and variances across groups, fitted to the data simultaneously. If configural invariance was confirmed, weak invariance was tested using a model (M1) with equal factor loadings but freely estimated indicator intercepts and variances. Evidence for the equivalence of factor loadings across groups is said to indicate ‘weak’ measurement invariance—i.e., that the latent variable is related to the items in the same way across groups. Next, if weak invariance was confirmed, strong invariance was tested in a model (M2) with equal factor loadings and equal indicator intercepts.‘Strong’ invariance is present when both item factor loadings and intercepts are equivalent across groups, indicating that observed mean group differences reflect latent mean group differences (Gregorich 2006). Finally, where strong invariance was present, a fourth model (M3) tested ‘strict’ measurement invariance (present when item residuals are equivalent across groups). Insofar as unique item variances influence the magnitude of correlations among observed variables, strict measurement invariance is required for comparing correlations among manifest variables across groups (Gregorich 2006). Models M1, M2 and M3 were each compared to the preceding model through a comparison of their X2 values. Measurement invariance holds when the X2 values obtained in the increasingly restrictive model do not differ significantly from the prior model.

  3. FIML is a model-based method for estimating parameters in the presence of missing data (Olinsky et al. 2003).FIML estimates parameters based on the available complete data as well as the implied values of the missing data given the observed data. It is conceptually similar to regression imputation and produces results similar to multiple imputation methods (see Graham et al. 1996; Olinsky et al. 2003).

  4. CFA analyses also assessed the fit of a three-factor structure previously validated by Hawkley et al. (2005) using a sample of 225 middle-aged adults (aged 50–68). Our analyses did not indicate acceptable fit of their three-factor structure within the current sample (χ2 = 479.380, df = 167, p < .001; CFI = 0.845, TLI = 0.824, RMSEA = 0.089, SRMR = 0.077).

  5. The use of a separate data set is generally recommended when CFA analyses follow EFA to identify a best-fitting factor structure (De Coster 1998). Often this is handled by splitting the sample in half and using one subsample for each component of the study. Given sample size limitations, we drew on the follow-up sample for the CFAs.

  6. CFA analyses did not indicate cross-loading involving item 14.

  7. CFA analyses did not reveal cross-loading involving item 6.

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Acknowledgments

This study was conducted as part of the Canadian Longitudinal Study on Aging (CLSA): Developmental Activities, Phase II, supported by grants from the Canadian Institutes of Health Research (CIHR; Raina et al. 2009) and the Michael Smith Foundation for Health Research through the BC Network of Aging Research (BCNAR). The authors wish to thank Drs. Holly Tuokko and Laura Funk for their comments on an earlier version of the manuscript.

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  1. Department of Sociology, University of Victoria, PO Box 3050, STN CSC, Victoria, BC, V8W 3P5, Canada

    Margaret J. Penning

  2. Centre for Health Services and Policy Research, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada

    Guiping Liu

  3. Centre on Aging, University of Victoria, PO Box 1700, STN CSC, Victoria, BC, V8W 2Y2, Canada

    Pak Hei Benedito Chou

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Penning, M.J., Liu, G. & Chou, P.H.B. Measuring Loneliness Among Middle-Aged and Older Adults: The UCLA and de Jong Gierveld Loneliness Scales. Soc Indic Res 118, 1147–1166 (2014). https://doi.org/10.1007/s11205-013-0461-1

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